{
 "cells": [
  {
   "cell_type": "code",
   "id": "initial_id",
   "metadata": {
    "collapsed": true,
    "ExecuteTime": {
     "end_time": "2025-01-07T11:37:04.408935Z",
     "start_time": "2025-01-07T11:37:04.397276Z"
    }
   },
   "source": [
    "import numpy as np\n",
    "from matplotlib import  pyplot as plt\n",
    "us_file_path = \"./youtube_video_data/US_video_data_numbers.csv\"\n",
    "uk_file_path = \"./youtube_video_data/GB_video_data_numbers.csv\"\n",
    "\n",
    "t_us = np.loadtxt(us_file_path,delimiter=\",\",dtype=\"int\")\n",
    "\n",
    "# 取美国的电影的评论的数据\n",
    "t_us_comments = t_us[:,-1]\n",
    "\n",
    "#选择比5000小的数据\n",
    "t_us_comments = t_us_comments[t_us_comments<=5000]\n",
    "\n",
    "print(t_us_comments.shape)\n",
    "\n",
    "#再看最小值和最大值\n",
    "print(t_us_comments.max(),t_us_comments.min())"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1373,)\n",
      "4995 0\n"
     ]
    }
   ],
   "execution_count": 4
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-01-07T11:37:19.244594Z",
     "start_time": "2025-01-07T11:37:19.096303Z"
    }
   },
   "cell_type": "code",
   "source": [
    "#剩余多少数据\n",
    "print(t_us_comments.shape)\n",
    "d = 50\n",
    "\n",
    "bin_nums = (t_us_comments.max()-t_us_comments.min())//d\n",
    "\n",
    "# 绘图\n",
    "plt.figure(figsize=(20,8),dpi=80)\n",
    "\n",
    "plt.hist(t_us_comments,bin_nums)\n",
    "\n",
    "plt.grid(True)\n",
    "plt.show()"
   ],
   "id": "c6194f42c4290df1",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1373,)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1600x640 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 5
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
